Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
This work addresses the need for accurate AI tools to support clinical decisions in lung infection diagnosis, offering an adaptable method for both CT scans and chest X-rays, though it appears incremental with hybrid innovations.
The paper tackled the problem of predicting lung infection severity from medical imaging by introducing a Transformer-based architecture with a custom data augmentation strategy, achieving state-of-the-art performance on benchmark datasets like RALO CXR and Per-COVID-19 CT.
Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity. Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel encoders, a cross-gated attention mechanism, and a feature aggregator to capture rich multi-scale features; and (ii) Conditional Online TransMix, a custom data augmentation strategy designed to address dataset imbalance by generating mixed-label image patches during training. Evaluated on two benchmark datasets, RALO CXR and Per-COVID-19 CT, our method consistently outperforms several state-of-the-art deep learning models. The results emphasize the critical role of data augmentation and gated attention in improving both robustness and predictive accuracy. This approach offers a reliable, adaptable tool to support clinical diagnosis, disease monitoring, and personalized treatment planning. The source code of this work is available at https://github.com/bouthainas/QCross-Att-PVT.